Triple
T3537731
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mario Kart 64 |
E74807
|
entity |
| Predicate | numberOfPlayableCharacters |
P48193
|
FINISHED |
| Object | 8 |
—
|
LITERAL FINISHED |
How this triple was built (2 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: 8 | Statement: [Mario Kart 64, numberOfPlayableCharacters, 8]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfPlayableCharacters Context triple: [Mario Kart 64, numberOfPlayableCharacters, 8]
-
A.
numberOfCharacters
Indicates the total count of individual characters present in a given text, string, or entity’s representation.
-
B.
hasPlayableCharacter
Indicates that an entity includes or is associated with a character that a user can control or play as.
-
C.
numberOfHumanProtagonists
Indicates the count of human characters that serve as protagonists in a given work or context.
-
D.
protagonistCount
Indicates the number of primary protagonists involved in a given narrative or work.
-
E.
numberOfKnownPlays
Indicates the total count of plays that are known to be associated with a given entity (such as an author, theater, or period).
- F. None of above. chosen
Provenance (4 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69ad85d274cc8190ab59c97298a1cfbf |
completed | March 8, 2026, 2:21 p.m. |
| NER | Named-entity recognition | batch_69adbcc928248190b851f8280d58cfcf |
completed | March 8, 2026, 6:15 p.m. |
| PD | Predicate disambiguation | batch_69adae13ab808190a5d6ecdc7543445e |
completed | March 8, 2026, 5:12 p.m. |
| PDg | Predicate description generation | batch_69adaed7f2ec819085467d281712e0e8 |
completed | March 8, 2026, 5:16 p.m. |
Created at: March 8, 2026, 3:20 p.m.